Special Issue "Advances in Numerical Optimization Methods for Machine-Learning"
Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 3775
Interests: numerical optimization; iterative methods for linear algebra; nonlinear inverse problems
Special Issues, Collections and Topics in MDPI journals
Optimization is at the heart of statistical and machine learning models. The recent increasing interest in numerical optimization methods for problems arising in big data and machine learning applications comes with many significant challenges. The features of good optimization algorithms from the machine learning and optimization perspectives can be quite different and improvements of classical optimization methods, as well as new procedures, are needed to cope with the problems that are highly nonlinear, nonconvex and high dimensional. Moreover, inherent noise in objective function and derivatives evaluations call for stochastic methods and adaptive strategies aimed at avoiding parameter tuning are also of interest.
The focus of this Special Issue is to highlight present advances on the interplay between numerical optimization methods and machine learning both from a theoretical and practical point of view.
Prof. Dr. Stefania Bellavia
Manuscript Submission Information
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- Numerical optimization
- Machine learning
- First and second order methods
- Noise-reduction procedures
- Complexity analysis